TY - GEN
T1 - Online dynamic risk calculator for early detection of stroke
AU - Siregar, Kemal N.
AU - Wijaya, Hendy Risdianto
AU - Supriyanto, Eko
AU - Salim, Maheza Irna Mohamad
AU - Eryando, Tris
AU - Syuhada, Wan Nor
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/4/9
Y1 - 2019/4/9
N2 - Stroke is one of the most common killers worldwide. The death of the most stroke patients is caused by late treatment. In order to prevent the late treatment, an online dynamic stroke risk calculator has been developed. The predictor considers three risk factor groups which are biomolecule related factors, life style related factors and physiological measure related factors. Age, gender, family history and own disease history are factors related to biomolecule structure. Food, water, and air intakes as well as environment, physical and mental activities are the main components of life style. Measurement of blood pressure, heart rate, blood glucose, body mass index, blood lipid are done to provide information on physiological measure related factors. In order to improve the accuracy of risk calculator, clinical assessment is recommended to be conducted if the risk factor is above human average risk at specific age. This includes to assess sign, symptom, stroke biomarker, as well as stroke related anatomy and pathophysiology imaging. All factors and their levels are stored in the cloud database. Online application to enter data and visualize results is connected to the cloud database. Rule-based algorithm and machine learning are applied to calculate the risk of getting stroke. More than 12,000 retrospective global data are stored in the database. The database and rule are dynamically updated by the new online data input to improve accuracy of prediction through learning process. The risk calculation (rule-based algorithm) has been compared with other algorithms on machine learning to prove the system model. The system has been also validated using 120 healthy data and 25 stroke patient data. Test result shows that the system produces more than 95% accuracy and can be a better dynamic stroke risk predictor that can be applied to machine learning. This system applies to early detection of stroke.
AB - Stroke is one of the most common killers worldwide. The death of the most stroke patients is caused by late treatment. In order to prevent the late treatment, an online dynamic stroke risk calculator has been developed. The predictor considers three risk factor groups which are biomolecule related factors, life style related factors and physiological measure related factors. Age, gender, family history and own disease history are factors related to biomolecule structure. Food, water, and air intakes as well as environment, physical and mental activities are the main components of life style. Measurement of blood pressure, heart rate, blood glucose, body mass index, blood lipid are done to provide information on physiological measure related factors. In order to improve the accuracy of risk calculator, clinical assessment is recommended to be conducted if the risk factor is above human average risk at specific age. This includes to assess sign, symptom, stroke biomarker, as well as stroke related anatomy and pathophysiology imaging. All factors and their levels are stored in the cloud database. Online application to enter data and visualize results is connected to the cloud database. Rule-based algorithm and machine learning are applied to calculate the risk of getting stroke. More than 12,000 retrospective global data are stored in the database. The database and rule are dynamically updated by the new online data input to improve accuracy of prediction through learning process. The risk calculation (rule-based algorithm) has been compared with other algorithms on machine learning to prove the system model. The system has been also validated using 120 healthy data and 25 stroke patient data. Test result shows that the system produces more than 95% accuracy and can be a better dynamic stroke risk predictor that can be applied to machine learning. This system applies to early detection of stroke.
KW - Dynamic System
KW - Early Detection
KW - Machine Learning
KW - Risk Calculator
KW - Risk Factor
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=85064823509&partnerID=8YFLogxK
U2 - 10.1063/1.5096725
DO - 10.1063/1.5096725
M3 - Conference contribution
AN - SCOPUS:85064823509
T3 - AIP Conference Proceedings
BT - 3rd Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, and Medical Devices
A2 - Wulan, Praswasti P.D.K.
A2 - Gozan, Misri
A2 - Astutiningsih, Sotya
A2 - Ramahdita, Ghiska
A2 - Dhelika, Radon
A2 - Kreshanti, Prasetyanugraheni
PB - American Institute of Physics Inc.
T2 - 3rd International Symposium of Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, and Medical Devices, ISBE 2018
Y2 - 6 August 2018 through 8 August 2018
ER -
